OpenRaiser/Intern-Atlas huggingface dataset:
iPhone's Digital Marketplace: Characterizing the Big Spenders
Venue: arXiv cs.SI 2017
Year: 2017

With mobile shopping surging in popularity, people are spending ever more money on digital purchases through their mobile devices and phones. However, few large-scale studies of mobile shopping exist. In this paper we analyze a large data set consisting of more than 776M digital purchases made on Apple mobile devices that include songs, apps, and in-app purchases. We find that 61% of all the spending is on in-app purchases and that the top 1% of users are responsible for 59% of all the spending. These big spenders are more likely to be male and older, and less likely to be from the US. We study how they adopt and abandon individual app, and find that, after an initial phase of increased daily spending, users gradually lose interest: the delay between their purchases increases and the spending decreases with a sharp drop toward the end. Finally, we model the in-app purchasing behavior in multiple steps: 1) we model the time between purchases; 2) we train a classifier to predict whether the user will make a purchase from a new app or continue purchasing from the existing app; and 3) based on the outcome of the previous step, we attempt to predict the exact app, new or existing, from which the next purchase will come. The results yield new insights into spending habits in the mobile digital marketplace.
Relay-Assisted Mixed FSO/RF Systems over Málaga-$\mathcal{M}$ and $κ$-$μ$ Shadowed Fading Channels
Venue: arXiv cs.IT 2017
Year: 2017

This letter presents a unified analytical framework for relay-assisted mixed FSO/RF transmission. In addition to accounting for different FSO detection techniques, the mathematical model offers a twofold unification of mixed FSO/RF systems by considering mixed Málaga-$\mathcal{M}$/$κ$-$μ$ shadowed fading, which includes as special cases nearly all linear turbulence/fading models adopted in the open literature.
On The Compound MIMO Wiretap Channel with Mean Feedback
Venue: arXiv cs.CR 2017
Year: 2017

Compound MIMO wiretap channel with double sided uncertainty is considered under channel mean information model. In mean information model, channel variations are centered around its mean value which is fed back to the transmitter. We show that the worst case main channel is anti-parallel to the channel mean information resulting in an overall unit rank channel. Further, the worst eavesdropper channel is shown to be isotropic around its mean information. Accordingly, we provide the capacity achieving beamforming direction. We show that the saddle point property holds under mean information model, and thus, compound secrecy capacity equals to the worst case capacity over the class of uncertainty. Moreover, capacity achieving beamforming direction is found to require matrix inversion, thus, we derive the null steering (NS) beamforming as an alternative suboptimal solution that does not require matrix inversion. NS beamformer is in the direction orthogonal to the eavesdropper mean channel that maintains the maximum possible gain in mean main channel direction. Extensive computer simulation reveals that NS performs very close to the optimal solution. It also verifies that, NS beamforming outperforms both maximum ratio transmission (MRT) and zero forcing (ZF) beamforming approaches over the entire SNR range. Finally, An equivalence relation with MIMO wiretap channel in Rician fading environment is established.
Sample Complexity of the Boolean Multireference Alignment Problem
Venue: arXiv cs.IT 2017
Year: 2017

The Boolean multireference alignment problem consists in recovering a Boolean signal from multiple shifted and noisy observations. In this paper we obtain an expression for the error exponent of the maximum A posteriori decoder. This expression is used to characterize the number of measurements needed for signal recovery in the low SNR regime, in terms of higher order autocorrelations of the signal. The characterization is explicit for various signal dimensions, such as prime and even dimensions.
Intelligent real-time MEMS sensor fusion and calibration
Venue: arXiv cs.RO 2017
Year: 2017

This paper discusses an innovative adaptive heterogeneous fusion algorithm based on estimation of the mean square error of all variables used in real time processing. The algorithm is designed for a fusion between derivative and absolute sensors and is explained by the fusion of the 3-axial gyroscope, 3-axial accelerometer and 3-axial magnetometer into attitude and heading estimation. Our algorithm has similar error performance in the steady state but much faster dynamic response compared to the fixed-gain fusion algorithm. In comparison with the extended Kalman filter the proposed algorithm converges faster and takes less computational time. On the other hand, Kalman filter has smaller mean square output error in a steady state but becomes unstable if the estimated state changes too rapidly. Additionally, the noisy fusion deviation can be used in the process of calibration. The paper proposes and explains a real-time calibration method based on machine learning working in the online mode during run-time. This allows compensation of sensor thermal drift right in the sensors working environment without need of re-calibration in the laboratory.
Towards Secure SPARQL Queries in Semantic Web Applications using PHP (Extended Version)
Venue: arXiv cs.CR 2017
Year: 2017

The Semantic Web (SW) is a significant advancement in the field of Internet technologies and an uncharted territory as far as security is concerned. In this paper we investigate and assess the impact of known attacks of SPARQL/SPARUL injections on Semantic Web applications developed in PHP. We highlight future challenges of developing robust Semantic Web applications using PHP. Our results demonstrate and quantify impacts on Confidentiality, Integrity and Availability (CIA) breaches of data in Semantic Web applications. Our recommendations are targeted to PHP developers, to encourage them to integrate security as early in their design and coding practice as possible.
Persistent Entropy for Separating Topological Features from Noise in Vietoris-Rips Complexes
Venue: arXiv cs.OH 2017
Year: 2017

Persistent homology studies the evolution of k-dimensional holes along a nested sequence of simplicial complexes (called a filtration). The set of bars (i.e. intervals) representing birth and death times of k-dimensional holes along such sequence is called the persistence barcode. k-Dimensional holes with short lifetimes are informally considered to be "topological noise", and those with long lifetimes are considered to be "topological features" associated to the filtration. Persistent entropy is defined as the Shannon entropy of the persistence barcode of a given filtration. In this paper we present new important properties of persistent entropy of Cech and Vietoris-Rips filtrations. Among the properties, we put a focus on the stability theorem that allows to use persistent entropy for comparing persistence barcodes. Later, we derive a simple method for separating topological noise from features in Vietoris-Rips filtrations.
On the Performance of Reduced-Complexity Transmit/Receive Diversity Systems over MIMO-V2V Channel Model
Venue: arXiv cs.IT 2017
Year: 2017

In this letter, we investigate the performance of multiple-input multiple-output techniques in a vehicle-to-vehicle communication system. We consider both transmit antenna selection with maximal-ratio combining and transmit antenna selection with selection combining. The channel propagation model between two vehicles is represented as n*Rayleigh distribution, which has been shown to be a realistic model for vehicle-to-vehicle communication scenarios. We derive tight analytical expressions for the outage probability and amount of fading of the post-processing signal-to-noise ratio.
A new lower bound for reset threshold of synchronizing automata with sink state
Venue: arXiv cs.FL 2017
Year: 2017

We present a new series of examples of binary slowly synchronizing automata with sink state. The reset threshold of the $n$-state automaton in this series is $\frac{n^2}{4}+2n-9$. This improves on the previously known lower bound for the maximum reset threshold of binary synchronizing $n$-state automata with sink state.
Securing Virtual Network Function Placement with High Availability Guarantees
Venue: arXiv cs.NI 2017
Year: 2017

Virtual Network Functions as a Service (VNFaaS) is currently under attentive study by telecommunications and cloud stakeholders as a promising business and technical direction consisting of providing network functions as a service on a cloud (NFV Infrastructure), instead of delivering standalone network appliances, in order to provide higher scalability and reduce maintenance costs. However, the functioning of such NFVI hosting the VNFs is fundamental for all the services and applications running on top of it, forcing to guarantee a high availability level. Indeed the availability of an VNFaaS relies on the failure rate of its single components, namely the servers, the virtualization software, and the communication network. The proper assignment of the virtual machines implementing network functions to NFVI servers and their protection is essential to guarantee high availability. We model the High Availability Virtual Network Function Placement (HA-VNFP) as the problem of finding the best assignment of virtual machines to servers guaranteeing protection by replication. We propose a probabilistic approach to measure the real availability of a system and design both efficient and effective algorithms that can be used by stakeholders for both online and offline planning.
LocDyn: Robust Distributed Localization for Mobile Underwater Networks
Venue: arXiv cs.MA 2017
Year: 2017

How to self-localize large teams of underwater nodes using only noisy range measurements? How to do it in a distributed way, and incorporating dynamics into the problem? How to reject outliers and produce trustworthy position estimates? The stringent acoustic communication channel and the accuracy needs of our geophysical survey application demand faster and more accurate localization methods. We approach dynamic localization as a MAP estimation problem where the prior encodes dynamics, and we devise a convex relaxation method that takes advantage of previous estimates at each measurement acquisition step; The algorithm converges at an optimal rate for first order methods. LocDyn is distributed: there is no fusion center responsible for processing acquired data and the same simple computations are performed for each node. LocDyn is accurate: experiments attest to a smaller positioning error than a comparable Kalman filter. LocDyn is robust: it rejects outlier noise, while the comparing methods succumb in terms of positioning error.
Development of Wind Power Generation Model with DFIG for Varying Wind Speed and Frequency Control for Wind Diesel Power Plant
Venue: arXiv cs.SY 2017
Year: 2017

The power generation with non-renewable energy sources has very harmful effects on the environment as well as these sources are depleting. On the other side the renewable energy sources are quite unpredictable source of power. The best trade-off is to use the combination of both kind of sources to make a hybrid system so that their individual power generation constraints can be overcome. The hybrid system taken for analysis in this work comprises of wind and diesel power generation systems. The complete modelling of the system has been done in MATLAB/SIMULINK environment. Doubly fed induction generator (DFIG) is used for power generation in wind power system. The modelling has been done considering the changing wind speed and varying load conditions. The mathematical models of DFIG and diesel power generator have been used to develop the simulink model which can be used for analysis of various performances of the system like frequency response and power sharing between different sources with load variation .The generating margin of DFIG is also simulated for the frequency support during varying load conditions .The generating margin is created by the control of active power output from DFIG. Also as the power demand rises the generating margin of DFIG keeps the balance between the power generation and load. Proportional Integral controller has been used for diesel generator plant for frequency control. The controller gains have been optimized with Particle Swarm Optimization technique. The proper selection of controller gains and wind power reserve help to achieve the enhanced frequency response of the hybrid system.
Application Embedding: A Language Approach to Declarative Web Programming
Venue: arXiv cs.PL 2017
Year: 2017

Since the early days of the Web, web application developers have aspired to develop much of their applications declaratively. However, one aspect of the application, namely its business-logic is constantly left imperative. In this work we present Application Embedding, a novel approach to application development which allows all aspects of an application, including its business-logic, to be programmed declaratively. We develop this approach in a two-step process. First, we draw a mapping between web applications and Domain-Specific Languages (DSLs). Second, we note that out of the two methods for implementing DSLs, namely as either internal or external, most traditional web applications correspond to external DSLs, while the the technique that corresponds to DSL embedding (implementing internal DSLs) is left mostly unexplored. By projecting the well-known technique of DSL embedding onto web applications, we derive a novel technique--Application Embedding. Application embedding offers a separation of code assets that encourages reuse of imperative code, while keeping all application-specific assets, including those specifying its business- logic, declarative. As validation, we implemented a simple, though nontrivial web application using the proposed separation of assets. This implementation includes an application-agnostic imperative host application named FishTank, intended to be applicable for a wide variety of web applications, and a declarative definition of the different aspects of the specific application, intended to be loaded on that host. Our method of separation of code assets facilitates a better separation of work, in comparison to traditional methods. By this separation, host application developers can focus mostly on the extra-functional aspects of a web application, namely on improving performance, scalability, and availability, while developers of an embedded application can focus on the functional aspects of their application, without worrying about extra- functional concerns. The reusability of the host application makes the effort put into a better implementation cost-effective, since it can benefit all applications built on top of it.
Interconnected Linguistic Architecture
Venue: arXiv cs.PL 2017
Year: 2017

The context of the reported research is the documentation of software technologies such as object/relational mappers, web-application frameworks, or code generators. We assume that documentation should model a macroscopic view on usage scenarios of technologies in terms of involved artifacts, leveraged software languages, data flows, conformance relationships, and others. In previous work, we referred to such documentation also as 'linguistic architecture'. The corresponding models may also be referred to as 'megamodels' while adopting this term from the technological space of modeling/model-driven engineering. This work is an inquiry into making such documentation less abstract and more effective by means of connecting (mega)models, systems, and developer experience in several ways. To this end, we adopt an approach that is primarily based on prototyping (i.e., implementa- tion of a megamodeling infrastructure with all conceivable connections) and experimentation with showcases (i.e., documentation of concrete software technologies). The knowledge gained by this research is a notion of interconnected linguistic architecture on the grounds of connecting primary model elements, inferred model elements, static and runtime system artifacts, traceability links, system contexts, knowledge resources, plugged interpretations of model elements, and IDE views. A corresponding suite of aspects of interconnected linguistic architecture is systematically described. As to the grounding of this research, we describe a literature survey which tracks scattered occurrences and thus demonstrates the relevance of the identified aspects of interconnected linguistic architecture. Further, we describe the MegaL/Xtext+IDE infrastructure which realizes interconnected linguistic architecture. The importance of this work lies in providing more formal (ontologically rich, navigable, verifiable) documentation of software technologies helping developers to better understand how to use technologies in new systems (prescriptive mode) or how technologies are used in existing systems (descriptive mode).
Adversarial Evaluation of Dialogue Models
Venue: arXiv cs.CL 2017
Year: 2017

The recent application of RNN encoder-decoder models has resulted in substantial progress in fully data-driven dialogue systems, but evaluation remains a challenge. An adversarial loss could be a way to directly evaluate the extent to which generated dialogue responses sound like they came from a human. This could reduce the need for human evaluation, while more directly evaluating on a generative task. In this work, we investigate this idea by training an RNN to discriminate a dialogue model's samples from human-generated samples. Although we find some evidence this setup could be viable, we also note that many issues remain in its practical application. We discuss both aspects and conclude that future work is warranted.
Energy-Efficient Memories using Magneto-Electric Switching of Ferromagnets
Venue: arXiv cs.ET 2017
Year: 2017

Voltage driven magneto-electric (ME) switching of ferro-magnets has shown potential for future low-energy spintronic memories. In this paper, we first analyze two different ME devices viz. ME-MTJ and ME-XNOR device with respect to writability, readability and switching speed. Our analysis is based on a coupled magnetization dynamics and electron transport model. Subsequently, we show that the decoupled read/write path of ME-MTJs can be utilized to construct an energy-efficient dual port memory. Further, we also propose a novel content addressable memory (CAM) exploiting the compact XNOR operation enabled by ME-XNOR device.
Entropic Causality and Greedy Minimum Entropy Coupling
Venue: arXiv cs.IT 2017
Year: 2017

We study the problem of identifying the causal relationship between two discrete random variables from observational data. We recently proposed a novel framework called entropic causality that works in a very general functional model but makes the assumption that the unobserved exogenous variable has small entropy in the true causal direction. This framework requires the solution of a minimum entropy coupling problem: Given marginal distributions of m discrete random variables, each on n states, find the joint distribution with minimum entropy, that respects the given marginals. This corresponds to minimizing a concave function of nm variables over a convex polytope defined by nm linear constraints, called a transportation polytope. Unfortunately, it was recently shown that this minimum entropy coupling problem is NP-hard, even for 2 variables with n states. Even representing points (joint distributions) over this space can require exponential complexity (in n, m) if done naively. In our recent work we introduced an efficient greedy algorithm to find an approximate solution for this problem. In this paper we analyze this algorithm and establish two results: that our algorithm always finds a local minimum and also is within an additive approximation error from the unknown global optimum.
Systems of natural-language-facilitated human-robot cooperation: A review
Venue: arXiv cs.RO 2017
Year: 2017

Natural-language-facilitated human-robot cooperation (NLC), in which natural language (NL) is used to share knowledge between a human and a robot for conducting intuitive human-robot cooperation (HRC), is continuously developing in the recent decade. Currently, NLC is used in several robotic domains such as manufacturing, daily assistance and health caregiving. It is necessary to summarize current NLC-based robotic systems and discuss the future developing trends, providing helpful information for future NLC research. In this review, we first analyzed the driving forces behind the NLC research. Regarding to a robot s cognition level during the cooperation, the NLC implementations then were categorized into four types {NL-based control, NL-based robot training, NL-based task execution, NL-based social companion} for comparison and discussion. Last based on our perspective and comprehensive paper review, the future research trends were discussed.
Pruned non-local means
Venue: arXiv cs.CV 2017
Year: 2017

In Non-Local Means (NLM), each pixel is denoised by performing a weighted averaging of its neighboring pixels, where the weights are computed using image patches. We demonstrate that the denoising performance of NLM can be improved by pruning the neighboring pixels, namely, by rejecting neighboring pixels whose weights are below a certain threshold $λ$. While pruning can potentially reduce pixel averaging in uniform-intensity regions, we demonstrate that there is generally an overall improvement in the denoising performance. In particular, the improvement comes from pixels situated close to edges and corners. The success of the proposed method strongly depends on the choice of the global threshold $λ$, which in turn depends on the noise level and the image characteristics. We show how Stein's unbiased estimator of the mean-squared error can be used to optimally tune $λ$, at a marginal computational overhead. We present some representative denoising results to demonstrate the superior performance of the proposed method over NLM and its variants.
Select Your Questions Wisely: For Entity Resolution With Crowd Errors
Venue: arXiv cs.DB 2017
Year: 2017

Crowdsourcing is becoming increasingly important in entity resolution tasks due to their inherent complexity such as clustering of images and natural language processing. Humans can provide more insightful information for these difficult problems compared to machine-based automatic techniques. Nevertheless, human workers can make mistakes due to lack of domain expertise or seriousness, ambiguity, or even due to malicious intents. The state-of-the-art literature usually deals with human errors via majority voting or by assigning a universal error rate over crowd workers. However, such approaches are incomplete, and often inconsistent, because the expertise of crowd workers are diverse with possible biases, thereby making it largely inappropriate to assume a universal error rate for all workers over all crowdsourcing tasks. To this end, we mitigate the above challenges by considering an uncertain graph model, where the edge probability between two records A and B denotes the ratio of crowd workers who voted Yes on the question if A and B are same entity. In order to reflect independence across different crowdsourcing tasks, we apply the well-established notion of possible worlds, and develop parameter-free algorithms both for next crowdsourcing, as well as for entity resolution problems. In particular, using our framework, the problem of entity resolution becomes equivalent to finding the maximum-likelihood clustering; whereas for the next crowdsourcing, we identify the record pair that maximally increases the reliability of the maximum-likelihood clustering. Based on detailed empirical analysis over real-world datasets, we find that our proposed solution, PERC (probabilistic entity resolution with imperfect crowd) improves the quality by 15% and reduces the overall cost by 50% for the crowdsourcing-based entity resolution problem.